Adaptive detection of chaotic oscillations in ferroresonance using modified extended Kalman filter

Power quality and power disturbances have become an important factor for power systems. Chaoticferroresonance is one of the disturbances that may cause overvoltages and overcurrents; hence, it can endanger the system reliability and continuous safe operating. A power system that generates chaotic oscillations is a dynamic system, which can be modeled with a Du ng equation. This paper introduces the application of a modified extended Kalman filter for improving the detection of chaotic behavior of power system signals. A modification algorithm is used to increase the estimation performance of the former casual extended Kalman filter. The proposed method is employed to distinguish the abnormalities from a signal contaminated with chaotic ferroresonance for promoting efficiency in power system characteristics detection.